| --- |
| license: mit |
| library_name: onnx |
| pipeline_tag: image-classification |
| tags: |
| - image-classification |
| - onnx |
| - efficientnet |
| - deepfake-detection |
| - ai-generated-image-detection |
| - cifake |
| - transfer-learning |
| - pytorch |
| - binary-classification |
| datasets: |
| - birdy654/cifake-real-and-ai-generated-synthetic-images |
| metrics: |
| - accuracy |
| model-index: |
| - name: CIFAKE-EfficientNet-B0 |
| results: |
| - task: |
| type: image-classification |
| name: Binary Image Classification |
| dataset: |
| name: CIFAKE |
| type: birdy654/cifake-real-and-ai-generated-synthetic-images |
| metrics: |
| - type: accuracy |
| value: 97.85 |
| name: Test Accuracy |
| --- |
| |
| # CIFAKE — Real vs AI-Generated Image Classifier |
|
|
| A binary image classifier that distinguishes **real photographs** from **AI-generated (synthetic) images**, built with **EfficientNet-B0** and transfer learning. |
|
|
| ## Model Description |
|
|
| This model was trained to detect AI-generated images using the [CIFAKE dataset](https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images), which contains 120,000 images (60K real from CIFAR-10 + 60K synthetic from Stable Diffusion v1.4). |
|
|
| - **Architecture**: EfficientNet-B0 (pretrained on ImageNet) |
| - **Task**: Binary classification (Real vs Fake) |
| - **Output**: Single logit → apply sigmoid to get P(Real) |
| - **Format**: ONNX (opset 17) |
| - **Input**: RGB image tensor of shape `[batch, 3, 224, 224]`, normalized with ImageNet statistics |
|
|
| ## Training Details |
|
|
| ### Two-Phase Training Strategy |
|
|
| | Phase | Epochs | Strategy | Learning Rate | Scheduler | |
| |-------|--------|----------|---------------|-----------| |
| | 1 | 10 | Feature extraction (backbone frozen) | 3e-4 | — | |
| | 2 | 5 | Full fine-tuning (all layers) | 3e-5 | CosineAnnealingLR | |
|
|
| - **Optimizer**: AdamW (weight_decay=1e-2) |
| - **Loss**: BCEWithLogitsLoss |
| - **Batch Size**: 128 |
| - **Mixed Precision**: FP16 via `torch.autocast` |
| |
| ### Preprocessing |
| |
| ```python |
| transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC, antialias=True) |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
| ``` |
| |
| ## Performance |
| |
| | Metric | Value | |
| |--------|-------| |
| | **Test Accuracy** | **97.85%** | |
| |
| Evaluated on 20,000 test images (10K real + 10K AI-generated). |
| |
| ## Usage |
| |
| ### Python (ONNX Runtime) |
| |
| ```python |
| import numpy as np |
| import onnxruntime as ort |
| from PIL import Image |
| from torchvision import transforms |
| from torchvision.transforms import InterpolationMode |
| |
| # Load model |
| session = ort.InferenceSession("model.onnx") |
| |
| # Preprocess image |
| transform = transforms.Compose([ |
| transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC, antialias=True), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
| ]) |
| |
| image = Image.open("your_image.jpg").convert("RGB") |
| input_tensor = transform(image).unsqueeze(0).numpy() |
| |
| # Run inference |
| logit = session.run(None, {"input": input_tensor})[0] |
| probability = 1 / (1 + np.exp(-logit)) # sigmoid |
|
|
| label = "REAL" if probability >= 0.5 else "FAKE" |
| confidence = float(probability) if label == "REAL" else float(1 - probability) |
| print(f"{label} ({confidence * 100:.2f}% confidence)") |
| ``` |
| |
| ## Labels |
| |
| | Index | Class | Description | |
| |-------|-------|-------------| |
| | 0 | FAKE | AI-generated synthetic image | |
| | 1 | REAL | Real photograph (from CIFAR-10) | |
| |
| The model outputs a single logit. After applying sigmoid: |
| - `sigmoid(logit) >= 0.5` → **REAL** |
| - `sigmoid(logit) < 0.5` → **FAKE** |
| |
| ## Dataset |
| |
| The [CIFAKE dataset](https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images) consists of: |
| |
| | Split | Real | Fake | Total | |
| |-------|------|------|-------| |
| | Train | 50,000 | 50,000 | 100,000 | |
| | Test | 10,000 | 10,000 | 20,000 | |
| |
| ## Citation |
| |
| ```bibtex |
| @article{bird2024cifake, |
| title={CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images}, |
| author={Bird, Jordan J and Lotfi, Ahmad}, |
| journal={IEEE Access}, |
| year={2024}, |
| doi={10.1109/ACCESS.2024.3356122} |
| } |
| ``` |
| |
| ## License |
| |
| This model is released under the [MIT License](https://opensource.org/licenses/MIT). |
| |
